Forecasting patient admission in orthopedic clinic at a hospital in Kuantan using autoregressive integrated moving average (ARIMA) models

Abstract

Abstract This study is an attempt to examine empirically the best ARIMA model for forecasting. The monthly time series data routinely-collected at Orthopedic clinic from January 2013 until June 2018 have been used for this purpose. At first the stationarity condition of the data series is observed by ACF and PACF plots, then checked using the Ljung-Box-Pierce Q-statistic. It has been found that the monthly time series data of the Orthopedic clinic are stationary. The best ARIMA model has been selected by using the MAPE. To select the best ARIMA model the data split into two periods, viz. estimation period and validation period. The model for which the values of MAPE are smallest is considered as the best model. Hence, ARIMA (1, 0, 0) is found as the best model for forecasting the Orthopedic clinic data series. The out of sample forecast by using ARIMA (1, 0, 0) model indicated a fluctuation of monthly orthopedic patients demand, from lowest was 294 and the highest was 299 patients that could receive treatment from the clinic in a month.